Data Missingness in Digital Phenotyping: Implications for Clinical Inference and Decision-Making

Background Digital phenotyping, the use of personal digital devices to capture and categorize real-world behavioral and physiological data, holds great potential for complementing traditional clinical assessments. However, missing data remains a critical challenge in this field, especially in longitudinal studies where missingness might obscure clinically relevant insights.

This paper examines the impact of data missingness on digital phenotyping clinical research, proposes a framework for reporting and accounting for data missingness, and explores its implications for clinical inference and decision-making.

We analyzed digital phenotyping data from a study involving 85 patients with chronic musculoskeletal pain, focusing on active (PROMIS-29 survey responses) and passive (accelerometer and GPS measures) data collected via the Beiwe Research Platform. We assessed data completeness and missingness at different timescales (day, hour, and minute levels), examined the relationship between data missingness and accelerometer measures and imputed GPS summary statistics, and studied the stability of regression models across varying levels of data missingness. We further investigated the association between functional status and day-level data missingness in PROMIS-29 subscores.

Data completeness showed substantial variability across timescales. Accelerometer-based cadence and imputed GPS-based home time and number of significant locations were generally robust to varying levels of data missingness. However, the stability of regression models was affected at higher thresholds (40% for cadence and 60% for home time). We also identified patterns wherein data missingness was associated with functional status.

Data missingness in clinical digital phenotyping studies impacts individual- and group-level analyses. Given these results, we recommend that studies account for and report data at multiple timescales (we recommend day, hour, and minute-level where possible), depending on the clinical goals of data collection. We propose a modified framework for categorizing missingness mechanisms in digital phenotyping, emphasizing the need for clinically relevant reporting and interpretation of missing data. Our framework highlights the importance of integrating clinical with statistical expertise, specifically to ensure that imputing missing data does not obscure but helps capture clinically meaningful changes in functional status. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was funded by the National Institute on Aging (1K01AG078127-01, PI: DSB) and the Brain & Behavior Research Foundation (29966, PI: DSB). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Mass General Brigham gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors after publication.